Learn how Pure Storage engineering manages streaming 190B log events per day and makes use of that deluge of data in our continuous integration (CI) pipeline. Our test infrastructure runs over 70,000 tests per day creating a large triage problem that would require at least 20 triage engineers. Instead, Spark’s flexible computing platform allows us to write a single application for both streaming and batch jobs to understand the state of our CI pipeline for our team of 3 triage engineers. Using encoded patterns, Spark indexes log data for real-time reporting (Streaming), uses Machine Learning for performance modeling and prediction (Batch job), and finds previous matches for newly encoded patterns (Batch job).

Resource allocation in this mixed environment can be challenging; a containerized Spark cluster deployment, and disaggregated compute and storage layers allow us to programmatically shift compute resources between the streaming and batch applications.

This talk will go over design decisions to meet SLAs of streaming and batching in hardware, data layout, access patterns, and containers strategy. We will also go over the challenges, lessons learned, and best practices for this kind of setup.

Learn how Pure Storage engineering manages streaming 190B log events per day and makes use of that deluge of data in our continuous integration (CI) pipeline. Our test infrastructure runs over 70,000 tests per day creating a large triage problem that would require at least 20 triage engineers. Instead, Spark’s flexible computing platform allows us to write a single application for both streaming and batch jobs to understand the state of our CI pipeline for our team of 3 triage engineers. Using encoded patterns, Spark indexes log data for real-time reporting (Streaming), uses Machine Learning for performance modeling and prediction (Batch job), and finds previous matches for newly encoded patterns (Batch job).

Resource allocation in this mixed environment can be challenging; a containerized Spark cluster deployment, and disaggregated compute and storage layers allow us to programmatically shift compute resources between the streaming and batch applications.

This talk will go over design decisions to meet SLAs of streaming and batching in hardware, data layout, access patterns, and containers strategy. We will also go over the challenges, lessons learned, and best practices for this kind of setup.

The increasingly competitive climate within Financial Services means that enhancing business outcomes by leveraging AI is essential. Data has never been more important to business success and a key aspect to optimising its value is in conjunction with AI.

In this session we will discuss the areas in which Financial Services are looking to leverage their data together with AI and some of the considerations for successful implementation of an AI infrastructure that accelerates time to value for these projects.

The key battleground for automotive stakeholders over the next three years will be achieving the production of electric, connected and autonomous vehicles at scale. Learning how to keep graphics processing units (GPUs) fueled with data when training the next generation of deep learning architectures is critical.

As GPU technology continues to advance, the demand for faster data continues to grow. In this 60-minute webinar, Ramnath Sai Sagar, AI and DL Product Lead at Pure Storage, presents a new benchmark suite for evaluating and tuning input pipelines. He examines results with TensorFlow’s DataSets API on a DGX-1 ‘supercomputer’ with V100 GPUs, and provide guidance on key tuning parameters and diagnostic techniques for improving performance.

Interested in learning about real-world AI use cases from a data scientist and founding engineer? That's exactly what we're covering in this exciting session featuring Joshua Robinson of Pure Storage. Tune in to discover:

- The top five lessons learned in making AI initiatives successful in real-world deployments
- How to navigate common pitfalls and challenges when deploying AI systems
- Compelling real-world examples of AI use cases for the enterprise
- and more!

Joshua Robinson is a Founding Engineer on the FlashBlade team at Pure Storage and is currently lead architect for advanced analytics and AI solutions. Prior to Pure, Joshua worked as a data scientist in the search infrastructure team at Google, focused on the data pipelines and machine-learning algorithms for selecting and crawling the Internet. Joshua graduated with a PhD in Electrical and Computer Engineering from Rice University in 2009 with a focus on machine learning and algorithms.

Join this exclusive panel session live from Big Data LDN to find out about the crucial role data plays in the highly competitive world of Formula One™. Executives from Mercedes AMG Petronas Motorsport, Pure Storage and TIBCO will discuss how its collection, storage and analysis can make the difference between victory and defeat.

In this panel session you’ll find out about the crucial role data plays in the highly competitive world of Formula One™ and how its collection, storage and analysis can make the difference between victory and defeat.

In today’s environment, business continuity is a necessity, and many businesses require 24x7x365 availability. Downtime is very expensive, not only in terms of lost revenue but also in the loss of customer confidence and trust. However, preventing downtime and ensuring business continuity can also be difficult and expensive. In this webcast, Michael Otey, senior contributing editor of IT Pro Today and President of TECA Inc will discuss the challenges of protecting your critical data and creating a highly available infrastructure. The lower RTOs and RPOs that you require, the more you typically need to spend.

Topics covered include:

- Business challenges of improving critical applications availability

- The latest industry trends in business continuity solutions

- Pros and cons of today’s primary availability technologies

Joining the webcast, Alex McMullan, Pure Storage EMEA CTO, will introduce a new way of providing business continuity for today’s businesses and critical applications. Alex will show how Pure Storage’s new Purity ActiveCluster is different from traditional storage solutions and represents a new type of Tier 1 availability solution for business critical and production applications.

What if backup appliances were no longer an entire industry, but simply an application running on a single, powerful platform? Whether it’s fast backup and recovery or rapid restore for test/dev, a modern data platform from Pure Storage can consolidate and accelerate these workloads.

With Pure Service Orchestrator, customers can now extend their shared infrastructure beyond existing scale-up and virtualised applications to support containerised, persistent applications – all on Shared Accelerated Storage infrastructure.

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